Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

MRI gradient waveform design by numerical optimization

O P Simonetti1, J L Duerk, V Chankong

  • 1Department of Radiology, MetroHealth Medical Center, Cleveland, Ohio.

Magnetic Resonance in Medicine
|April 1, 1993
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Guidelines for training in cardiovascular magnetic resonance (CMR).

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance·2018
Same author

Edge Sharpness Assessment by Parametric Modeling: Application to Magnetic Resonance Imaging.

Concepts in magnetic resonance. Part A, Bridging education and research·2016
Same author

Control of intravascular catheters using an array of active steering coils.

Medical physics·2011
Same author

Magnetic resonance imaging-guided renal artery stent placement in a Swine model: comparison of two tracking techniques.

Acta radiologica (Stockholm, Sweden : 1987)·2008
Same author

Real-time cardiac cine imaging with SPIDER: steady-state projection imaging with dynamic echo-train readout.

Magnetic resonance in medicine·2001
Same author

Modified block uniform resampling (BURS) algorithm using truncated singular value decomposition: fast accurate gridding with noise and artifact reduction.

Magnetic resonance in medicine·2001
Same journal

Dependence of the Extra-Cellular Diffusion Coefficient on the Fractions of Neurites and Cell Bodies in Gray Matter.

Magnetic resonance in medicine·2026
Same journal

Triple-Pulse <sup>23</sup>Na MRI Sequence (TriNa) for Simultaneous Acquisition of Spin-Density-Weighted and Fluid-Attenuated Images.

Magnetic resonance in medicine·2026
Same journal

Evaluation of Phantom Doping Materials in Quantitative Susceptibility Mapping.

Magnetic resonance in medicine·2026
Same journal

Design of an 8-Channel Transmit 32-Channel Receive 11.7T Head Coil and Evaluation of SNR Gains.

Magnetic resonance in medicine·2026
Same journal

The Potential for Absolute Temperature Imaging Based on Brain Metabolites Using an FID-Shifting Approach in Gradient Echo Planar Spectroscopic Imaging (GREPSI).

Magnetic resonance in medicine·2026
Same journal

Prospective Head Motion Correction in T1- and T2-Weighted Long Echo Train Sequences Using Servo Navigation.

Magnetic resonance in medicine·2026
See all related articles

This study presents a new method for designing gradient waveforms using nonlinear optimization. This approach creates physically realizable waveforms, improving imaging quality and reducing design time.

Area of Science:

  • Medical Imaging
  • Magnetic Resonance Imaging (MRI)

Background:

  • Gradient waveform design is crucial for MRI performance.
  • Existing methods may impose artificial constraints on waveform shape.
  • Optimization is key to achieving specific imaging goals.

Purpose of the Study:

  • To introduce a novel method for gradient waveform design using nonlinear constrained optimization.
  • To generate physically realizable waveforms that optimize imaging conditions and reduce motion artifacts.
  • To compare waveforms designed with different objectives.

Main Methods:

  • Formulation and solution of waveform optimization problems.
  • Minimization of root mean squared current and waveform moments.
  • Defining waveforms as discrete amplitudes to avoid artificial shape constraints.

Related Experiment Videos

Main Results:

  • Generated waveforms are physically realizable and optimal for specific imaging goals.
  • The method allows for guaranteed optimality concerning chosen objectives.
  • Reduced reliance on designer experience and potential decrease in design time.

Conclusions:

  • Nonlinear constrained optimization offers an effective approach to gradient waveform design.
  • This method enhances MRI performance by producing optimal, artifact-reducing waveforms.
  • The approach democratizes waveform design, making it more accessible and efficient.